SYSTEM AND METHOD FOR PREDICTION OF GAS TURBINE TRIPS DUE TO THERMOCOUPLE FAILURES

• US 20090055070A1
• Filed: 08/23/2007
• Published: 02/26/2009
• Est. Priority Date: 08/23/2007
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Jammu, Vinay Bhaskar, De, Soumen, Pandey, Achalesh Kumar, Babu, Ravi Yoganatha, Rucigay, Richard John
• Granted Patent: US 7,734,443 B2
• Time in Patent Office: Days
• US Class Current: 701/100
• CPC Class Codes: F02C 9/00 Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05
• CPC Class Codes: F05D 2260/80 Diagnostics
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06

Abstract

Systems and methods for prediction of gas turbine trips due to component failures such as thermocouple failures. Exemplary embodiments include prediction of gas turbine trips due to component failures, including collecting raw gas turbine operational data and using the raw gas turbine operational data to generate a prognostic indicator for the prediction of a turbine trip due to the failed thermocouples.

Claims

1. A method for the prediction of gas turbine trips due to thermocouple failures, the method comprising:

collecting raw gas turbine operational data; and

using the raw gas turbine operational data to generate a prognostic indicator for the prediction of a turbine trip due to the failed thermocouples.

2. The method as claimed in claim 1, further comprising:

extracting thermocouple features indicative of a turbine trip from the raw gas turbine operational data;

applying a rule set to the extracted features;

determining a fused belief of thermocouple failure, based on an apriori probability and the extracted features; and

classifying the predicted thermocouple failure,

wherein extracting features from the raw gas turbine operational data includes applying domain knowledge to the extracted features.

3. The method as claimed in claim 2 further comprising calculating apriori probability and applying the domain knowledge to the calculated apriori probability.

4. The method as claimed in claim 3 wherein determining a fused belief of failure further comprises applying the calculated apriori probability to the determined fused belief of failure.

5. The method as claimed in claim 1 wherein thermocouple features indicative of a turbine trip include at least one of a maximum standard deviation based on thermocouple fluctuations, a cumulative value of combustion in excess of a maximum threshold, a cumulative value of a thermocouple trouble alarm, and a cumulative value of a spread2 exceedance.

6. A system for the prediction of gas turbine trips due to thermocouple failures, the system comprising:

a turbine;

a local computer coupled to the turbine for collecting raw turbine operational data related to a thermocouple failure from the turbine;

a remote computer coupled to the local computer;

a process, residing on the local computer, for computing a prediction of trips, the process including instructions to generate a prognostic indicator for the prediction of a turbine trip due to the failed thermocouples; and

a database coupled to the remote computer for storing information related to a trip of the turbine.

7. The system as claimed in claim 6 wherein the local computer includes a first process for extracting features from the raw turbine operational data, the raw turbine operation data and the features being indicative of thermocouple failure.

8. The system as claimed in claim 7 wherein the remote computer includes a second process for applying a rule set to the extracted features.

9. The system as claimed in claim 8 wherein the second process includes instructions to:

perform a trip reduction analysis; and

generate recommendations to prevent a trip.

10. The system as claimed in claim 8 wherein the database comprises a computer readable medium having a plurality of data nodes stored on the medium and representing a data structure, comprising:

a first data node stored in a first region of a range of memory addresses in the medium, the first data node representing a time to trip indicator;

a second data node stored in a second region of a range of memory addresses in the medium, the second data node representing trip mitigation recommendations; and

an index stored in an index region of the range of memory addresses, the index storing relationship information between the first and second data nodes, wherein the second process includes instructions to generate appropriate action to mitigate the trip.

11. The system as claimed in claim 10 wherein the appropriate action to mitigate the trip is at least one of a graceful shutdown of the turbine and a repair of the failed thermocouple.

12. The method as claimed in claim 7 wherein thermocouple features indicative of a turbine trip include at least one of a maximum standard deviation based on thermocouple fluctuations, a cumulative value of combustion in excess of a maximum threshold, a cumulative value of a thermocouple trouble alarm, and a cumulative value of a spread2 exceedance.

13. In a power plant having a turbine, a method for the prediction of gas turbine trips due to thermocouple failures, the method comprising:

collecting thermocouple failure data having failure signatures from the turbine for collection in a remote computer;

processing the thermocouple failure data to extract features based on the failure signatures and based on domain knowledge of the turbine;

applying a rule set on the extracted features based on apriori probability and the domain knowledge; and

determining a fused belief of failure calculated from the rule set applied to the extracted features.

14. The method as claimed in claim 13 further comprising classifying the component failure from the fused belief of failure.

15. The method as claimed in claim 14 further comprising generating a prognostic indicator.

16. The method as claimed in claim 15 wherein the prognostic indicator includes a band of at least one of no trip, low probability of trip, and high probability of trip.

17. The method as claimed in claim 13 wherein the apriori probability is calculated in the remote computer based sensor fusion methods including at least a Bayesian Fusion as follows:

18. The method as claimed in claim 13 wherein thermocouple features indicative of a turbine trip include at least one of a maximum standard deviation based on thermocouple fluctuations, a cumulative value of combustion in excess of a maximum threshold, a cumulative value of a thermocouple trouble alarm, and a cumulative value of a spread2 exceedance.

19. The method as claimed in claim 13 further comprising generating an appropriate action to mitigate a trip of the gas turbine.

20. The method as claimed in claim 19 wherein the appropriate action to mitigate the trip is at least one of a graceful shutdown of the turbine and a repair of the failed component.